text: # Copyright (C) 2018-2021 Intel Corporation
  # SPDX-License-Identifier: Apache-2.0
  
  import numpy as np
  
  from extensions.ops.mvn import MVN
  from extensions.ops.range import Range
  from mo.front.common.partial_infer.utils import int64_array
  from mo.front.tf.graph_utils import create_op_node_with_second_input, create_op_with_const_inputs
  from mo.graph.graph import Graph
  from mo.middle.replacement import MiddleReplacementPattern
  from mo.ops.const import Const
  from mo.ops.reshape import Reshape
  from mo.ops.shape import Shape
  
  
  class FusedBatchNormTraining(MiddleReplacementPattern):
      """
      Transformation looks for the BatchNorm layers in training mode and does the following:
      1. Fuses batch dimension with one of the spatial dimensions of the input to BatchNorm because batch normalization is
      performed over batch dimension also (per channel(features) dimension).
      2. Inserts MVN layer.
      3. Reshape MVN output back to the original one.
      """
      enabled = True
      replacement_id = "Fused_Batch_Norm_is_training_true"
      force_shape_inference = True
      force_clean_up = True
      # transformation works for the NHWC layout because transformation inserts Reshape to fuse N and H dimensions
      graph_condition = [lambda graph: graph.graph['layout'] == 'NHWC']
  
      def pattern(self):
          return dict(
              nodes=[
                  ('op', dict(kind='op', op=lambda op: op in ['FusedBatchNorm', 'FusedBatchNormV2', 'FusedBatchNormV3'],
                              is_training=True))],
              edges=[]
          )
  
      def replace_pattern(self, graph: Graph, match: dict):
          node = match['op']
          node_name = node.soft_get('name', node.id)
          node.is_training = False
  
          shape = node.in_port(1).data.get_shape()
          assert shape is not None, 'The shape of scale input of the BatchNorm node {} is not defined'.format(node.name)
  
          bn_mean = Const(graph, {'name': node_name + '/mean', 'value': np.zeros(shape, dtype=np.float32),
                                  'override_output_shape': True}).create_node()
          bn_std = Const(graph, {'name': node_name + '/std', 'value': np.ones(shape, dtype=np.float32),
                                 'override_output_shape': True}).create_node()
          node.in_port(3).get_connection().set_source(bn_mean.out_port(0))
          node.in_port(4).get_connection().set_source(bn_std.out_port(0))
  
          # save the original shape
          original_shape = Shape(graph, {'name': node.in_port(0).get_source().node.soft_get('name')}).create_node()
          original_shape.in_port(0).connect(node.in_port(0).get_source())
  
          input_rank = len(node.in_port(0).data.get_shape())
          rng = create_op_with_const_inputs(graph, Range,
                                            {0: int64_array(1), 1: int64_array(input_rank - 1), 2: int64_array(1)},
                                            {'name': node_name + '/Range', 'output_type': np.int64})
          mvn = MVN(graph, {'name': node_name + '/mvn_', 'eps': node.soft_get('eps', 1e-6), 'eps_mode': 'inside_sqrt',
                            'normalize_variance': 1, 'override_output_shape': True}).create_node()
          node.in_port(0).get_connection().insert_node(mvn)
          mvn.in_port(1).connect(rng.out_port(0))
  
          reshape_4d = create_op_node_with_second_input(graph, Reshape, int64_array([1, -1, 0, 0]),
                                                        {'override_output_shape': True,
                                                         'name': node_name + '/fused_batch_and_channels'})
          mvn.in_port(0).get_connection().insert_node(reshape_4d)
  
          # restore original shape
          reshape_back = Reshape(graph, {'name': node_name + '/restore_shape',
                                         'override_output_shape': True}).create_node()
          reshape_back.in_port(1).connect(original_shape.out_port(0))
          mvn.out_port(0).get_connection().insert_node(reshape_back)
  